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diffusers-qa-chatbot-artifacts

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>>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png"
>>> response = requests.get(url)
>>> mask_image = Image.open(BytesIO(response.content))
>>> mask_image = mask_image
>>> pipe = IFInpaintingPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "blue sunglasses"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... mask_image=mask_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... mask_image=mask_image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png") encode_prompt < source > ( prompt: Union do_classifier_free_guidance: bool = True num_images_per_prompt: int = 1 device: Optional = None negative_prompt: Union = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None clean_caption: bool = False ) Paramete...
prompt to be encoded do_classifier_free_guidance (bool, optional, defaults to True) β€”
whether to use classifier free guidance or not num_images_per_prompt (int, optional, defaults to 1) β€”
number of images that should be generated per prompt
device β€” (torch.device, optional):
torch device to place the resulting embeddings on negative_prompt (str or List[str], optional) β€”
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds. instead. If not defined, one has to pass negative_prompt_embeds. instead.
Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1). prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input
argument. clean_caption (bool, defaults to False) β€”
If True, the function will preprocess and clean the provided caption before encoding. Encodes the prompt into text encoder hidden states.
DDIM Denoising Diffusion Implicit Models (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon. The abstract from the paper is: Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce...
A UNet2DModel to denoise the encoded image latents. scheduler (SchedulerMixin) β€”
A scheduler to be used in combination with unet to denoise the encoded image. Can be one of
DDPMScheduler, or DDIMScheduler. Pipeline for image generation. This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). __call__ < source > ( batch_size: int = 1 generator: Union = None eta: float = 0.0 num_inference_steps: int = 50 use_clipped_model_output: Optional = None output_type: Optional = 'pil' return_dict: bool = True ) β†’ ImagePipelin...
The number of images to generate. generator (torch.Generator, optional) β€”
A torch.Generator to make
generation deterministic. eta (float, optional, defaults to 0.0) β€”
Corresponds to parameter eta (Ξ·) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers. A value of 0 corresponds to
DDIM and 1 corresponds to DDPM. num_inference_steps (int, optional, defaults to 50) β€”
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. use_clipped_model_output (bool, optional, defaults to None) β€”
If True or False, see documentation for DDIMScheduler.step(). If None, nothing is passed
downstream to the scheduler (use None for schedulers which don’t support this argument). output_type (str, optional, defaults to "pil") β€”
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) β€”
Whether or not to return a ImagePipelineOutput instead of a plain tuple. Returns
ImagePipelineOutput or tuple
If return_dict is True, ImagePipelineOutput is returned, otherwise a tuple is
returned where the first element is a list with the generated images
The call function to the pipeline for generation. Example: Copied >>> from diffusers import DDIMPipeline
>>> import PIL.Image
>>> import numpy as np
>>> # load model and scheduler
>>> pipe = DDIMPipeline.from_pretrained("fusing/ddim-lsun-bedroom")
>>> # run pipeline in inference (sample random noise and denoise)
>>> image = pipe(eta=0.0, num_inference_steps=50)
>>> # process image to PIL
>>> image_processed = image.cpu().permute(0, 2, 3, 1)
>>> image_processed = (image_processed + 1.0) * 127.5
>>> image_processed = image_processed.numpy().astype(np.uint8)
>>> image_pil = PIL.Image.fromarray(image_processed[0])
>>> # save image
>>> image_pil.save("test.png") ImagePipelineOutput class diffusers.ImagePipelineOutput < source > ( images: Union ) Parameters images (List[PIL.Image.Image] or np.ndarray) β€”
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). Output class for image pipelines.
How to run Stable Diffusion with Core ML Core ML is the model format and machine learning library supported by Apple frameworks. If you are interested in running Stable Diffusion models inside your macOS or iOS/iPadOS apps, this guide will show you how to convert existing PyTorch checkpoints into the Core ML format and...
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β”‚ └── packages
└── split_einsum
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